Characteristics of education production functions: An application of canonical regression analysis

Gyimah‐Brempong, Kwabena; Gyapong, Anthony O.

doi:10.1016/0272-7757(91)90035-n

Cited by 39 publications

(10 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…78 An alternative statistical approach to dealing with multiple outputs is the estimation of canonical correlation models (e.g., Gyimah-Brempong and Byapong (1991)). These methods use statistical criteria to maximize Finally, by going to the dual of the production problem it is possible to frame the estimation in terms of cost functions (e.g., Downes and Pogue (1994) or Duncombe, Ruggiero, and Yinger (1996)).…”

mentioning

confidence: 99%

Publicly Provided Education

Hanushek

2002

183

171

View full text Add to dashboard Cite

Historically, most attention in public programs has been given to the resources devoted to the activity, and resources have been used to index both commitment and quality. Education differs from other areas of public expenditure because direct measures of outcomes are available, making it is possible to consider results and, by implication, to consider the efficiency of provision. Early interpretations of the evidence, emanating from popular interpretations of the Coleman Report that "schools do not make a difference," are incorrect, but the basic evidence behind the statement suggests serious performance problems of government supply, because purchased inputs to schools are not closely related to outcomes. This paper reviews that evidence along with providing an evaluation of the various controversial aspects including issues of causality, consumer behavior, and estimation approaches. Two detailed policy areas are discussed in terms of the evidence on performance: public versus private provision and the financing of schools.

show abstract

mentioning

confidence: 99%

Publicly Provided Education

Hanushek

2002

183

171

View full text Add to dashboard Cite

show abstract

“…While district level aggregation has previously been employed in a number of production functions models (e.g., Ferguson & Ladd, 1996;Gyimah-Brempong & Gyapong, 1991;Sebold & Dato, 1981), some objections have been raised to this approach on grounds of omitted variable biases (Hanushek, Rivkin, & Taylor, 1996). However, district-level analyses can be useful despite these methodological criticisms (Ferguson & Ladd, 1996), particularly for highlighting the general relationships between resource inputs and student outcomes.…”

Section: Methodsmentioning

confidence: 99%

Public expenditures and the production of education

Neely

Diebold

2016

EPAA

View full text Add to dashboard Cite

The relationship between public education expenditures and student outcomes remains an important concern for policy analysts, educational administrators, and the public at large. While previous studies have failed to identify a consistent relationship between public investments in education and positive student outcomes, most analyses have not accounted for the different educational goals associated with various instructional expenditure categories. This study builds on prior research by using Pennsylvania’s public school districts to test proposed improvements in model specification for the traditional education production function. Using longitudinal, fixed-effects models, a detailed disaggregation of instructional expenditures is undertaken in order to account for the likelihood that different instructional subcategories (i.e. regular programming, special education, and vocational instruction) influence student outcomes in varying ways. The results suggest that the impact of expenditures may be understated in previous studies based on a failure to account for these distinctions, particularly in the case of Mathematics education.

show abstract

“…Gyimah-Brempong and Gyapong [11] examined the effects of socioeconomic characteristics of communities in the production of high school education in the state of Michigan. Rovai and Ponton [12] investigated how a set of three classroom community variables was related to a set of two students learning variables in a predominantly White sample of 108 online African American and Caucasian graduate students using CCA.…”

Section: Introductionmentioning

confidence: 99%

Application of Canonical Correlation Analysis to study the influence of mathematics on engineering programs: A case study

Nanayakkara

Peiris

2016

2016 Moratuwa Engineering Research Conference (MERCon)

View full text Add to dashboard Cite

Mathematical knowledge is essential to improve the analytical thinking of engineering undergraduates. Exploring more information from existing academic data is an essential aspect of the educational research. The objective of this study is to explore the impact of mathematics performance on different engineering programs. The study was conducted with 626 engineering students from seven different disciplines at the Faculty of Engineering, University of Moratuwa, Sri Lanka. Canonical Correlation Analysis (CCA) was employed to investigate the relationship between mathematics courses and other engineering courses with respect to their disciplines. Results of CCA revealed that the mathematics performance in both semester 1 and 2 influences significantly on the students' academic performance in Level 2 of the seven engineering disciplines considered. Wilk's lambda test statistic confirmed that only the first canonical variate pair is significant for all disciplines. The squared canonical correlations of first canonical variate pair indicated that the amount of variance between the mathematics performance and academic performance in Level 2 explained varied among seven disciplines from 42% to 68%. The impact is higher from mathematics in semester 2 than that from semester 1 in all disciplines except for Material Science and Engineering discipline. The explainable variability of student academic performance in Level 2 by the canonical variate of mathematics is varied from 27% to 50% among seven disciplines. Based on preliminary analysis, it can be concluded that the performance in mathematics in Level 1 could indicate the trend towards the student academic performance in all engineering programs.

show abstract

Characteristics of education production functions: An application of canonical regression analysis

Cited by 39 publications

References 17 publications

Publicly Provided Education

Publicly Provided Education

Public expenditures and the production of education

Application of Canonical Correlation Analysis to study the influence of mathematics on engineering programs: A case study

Contact Info

Product

Resources

About